Loading summary
Transcript4 lines
- [00:00]
A
Hi, I'm Michael Zesus, Global Head of Fixed Income Research and Public Policy Strategy at Morgan Stanley. Before we get into today's episode, the team behind Thoughts on the Market wants your thoughts and your input. Fill out our listener survey and help us make this podcast even more valuable for you. The link is in the show notes and you'll hear it at the end of the episode. Plus help us help the Feeding America organization. For every survey completed, Morgan Stanley will donate $25 toward their important work. Thanks for your time and support onto the show.
- [00:33]
B
Welcome to Thoughts on the Market. I am Vishi Tirupator, Morgan Stanley's Chief Fixed Income Strategist. Today I'll be talking about the macro implications of the Deep Seq development. It's Friday, February 7th at 9am and I'm on the road in Riyadh, Saudi Arabia. Recently we learned that Deepseek, a Chinese AI startup, has developed two open source large language models, LLMs, that can perform at levels comparable to models from American counterparts at a substantially lower cost. This news set off shockwaves in the equity markets that wiped out nearly a trillion dollars in the market cap of listed US technology companies on January 27. While the market has recouped some of these losses, their magnitude raises questions for investors about AI. My equity research colleagues have addressed a range of stock specific issues in their work. Today we step back and consider the broader implications for the economy in terms of productivity growth and investment spending on AI infrastructure. First thing, While this is an important milestone and a significant development in the evolution of LLMs, it doesn't entirely come as a shock. The history of computing is replete with examples of dramatic efficiency gains. The Deep SEQ development is precisely that, a dramatic efficiency improvement which in our view drives incremental demand for AI. Rapid declines in the cost of computing during the 1990s provide a useful parallel to what we are seeing now. As Michael Gapin, our US chief economist, has noted, the investment boom during the 1990s was really driven by the pace at which firms replaced depreciated capital and a sharp and persistent decline in the price of computing capital relative to the price of output. If efficiency gains from Deepseek reflect a similar phenomenon, we may be seeing early signs the cost of AI capital is coming down and coming down rapidly in turn. That should support the outlook for business spending pertaining to AI. In the last few weeks we've heard a lot of reference to the Jevons Paradox, which really dates from 1865, and it states that as technological advancements reduce the cost of using a resource, the overall demand for the resource increases, causing the total resource consumption to rise. In other words, cheaper and more ubiquitous technology will increase its consumption. This enables AI to transition from innovators to more generalized adoption and opens the door for faster LLM enabled product innovation. That means wider and faster consumer and enterprise adoption. Over time, this should result in greater increases in productivity and faster realization of AI's transformational promise. From a micro perspective, our equity research colleagues who are experts in covering stocks in these sectors, come to a very similar conclusion. They think it's unlikely that the Deep SEQ development will meaningfully reduce capex related to AI infrastructure. From a macroeconomic perspective, there's a good case to be made for higher business spending related to AI as well as productivity growth from AI. Obviously it's still early days and we will see leaders and laggards at the stock level, but the economy as a whole, we think, will emerge as a winner. Deep SEQ illustrates the potential for efficiency gains, which in turn foster greater competition and drive wider adoption of AI. With that premise, we remain constructive on AI's transformational promise. Thanks for listening. If you enjoyed the podcast, help us make it even more valuable to you. Share your feedback on the show@morgostanley.com podcast survey or head to the episode notes for the survey link.
- [04:31]
C
The preceding content is informational only and based on information available when created. It is not an offer or solicitation, nor is it tax or legal advice. It does not consider your financial circumstances and objectives and may not be suitable for you.
- [04:47]
B
In the last few weeks, it's almost like the birds are waiting for me to start speaking.